Implementation of Manuscript "Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning"

How to use

Our DANTE approach:

1. Global artificial learning:

Modify the server_dir based on your own environment. Then, run: python train_electric_cifar10_readout.py -e 3b -g gpu_idx -l 0.01 -ep 200

2. Local optical learning:

Modify the server_dir, folder_to_fit, and gpu index based on your own environment. Then, run: python train_electric_optical_kernel.py

3. test accuracy:

Modify the folder_to_fit based on your own environment. Then, run: python test_electric_optical_kernel.py -e 3bs -g gpu_index

Existing approach:

Modify the server_dir based on your own environment.

run: python train_end2end_cifar10_readout.py -e 33l -g gpu_idx -l 0.01 -bs 32 for ONN-3-3

run: python train_end2end_cifar10_readout.py -e 37l -g gpu_idx -l 0.01 -bs 8 for ONN-3-7